1. Field of the Invention
The present invention relates to an apparatus for detecting periodicity in time-series data, which is constituted by a plurality of discrete time periods and which is to be processed.
More specifically, the present invention relates to an apparatus for detecting periodicity information about the periodicity in time-series data which includes plural kinds of of time periods in an arbitrary data format, e.g., information about some rhythm patterns included in sound signals of music, in accordance with a simplified operating process, by means of a computer system.
Recently, in many fields in which various kinds of signals for transferring time-series data are handled, it has become necessary to accurately process the above-mentioned time-series data in a relatively short time, e.g., in real time. Typically, in the case where a certain music played by a man is to be analyzed in real time by utilizing a computer system, so that a smooth communication between a man and a computer system can be realized, the computer system is required to listen to the music and to rapidly extract some rhythm patterns included in sound signals constituting the music.
To satisfy such a requirement, it is essential to rapidly detect periodicity information about the periodicity in time-series data, and to understand what kinds of elements the signals which are to be processed include, by utilizing a computer system.
Namely, to satisfy the above-mentioned requirement, it is desirable to establish a configuration in which a process for detecting periodicity information about the periodicity in time-series data can be easily executed without the necessity for presupposing the data format of the time-series data, and in which the time-series data can be accurately processed in real time.
2. Description of the Related Art
In a first technique for detecting such periodicity information according to the prior art, some data patterns are presumed in advance and stored in a memory, e.g., a RAM (Random Access Memory), by utilizing a computer system. Further, the computer system carries out a process for matching the stored data patterns with time-series data, which is to be input to the computer system. Finally, by extracting data patterns, conforming to the stored data patterns, from the time-series data, periodicity information about the periodicity in the time-series data which is to be processed can be detected.
In a second technique for detecting the periodicity information according to the prior art, a history of time-series data in the past is stored in a memory. Further, an autocorrelation function is calculated between the historic time-series data in the past and the time-series data which is to be input to the computer system. Finally, on the basis of the thus calculated autocorrelation function, periodicity information about the periodicity in the time-series data which is to be processed can be detected, similar to the case of the first technique.
However, according to the above-mentioned first technique, it is necessary to presume in advance possible data patterns. More specifically, the above-mentioned process for matching the stored data patterns with the incoming time-series data in the first technique is usually executed by using a method of an FFT (Fast Fourier Transformation). In general, it takes relatively long time to carry out an analysis of the time-series data by using the FFT. Therefore, it is very difficult to adequately handle all the periodicity information about the periodicity in time-series data in real time, even with the aid of a workstation operating at high speed.
Furthermore, in regard to time-series data which does not have these data patterns, a problem occurs in that it is impossible to detect periodicity information.
On the other hand, according to the above-mentioned second technique, an analysis of the time-series data by using the autocorrelation function can be executed at higher speed than the analysis of the time-series data by using the FFT. However, according to the second technique, it is necessary to store in advance a large amount of long-term time-series data in the past.
Further, it is also necessary to execute the a troublesome operating process including the calculation of the autocorrelation function for which the amount of computation is required. Therefore, another problem occurs in that it is almost impossible to detect periodicity information in real time, especially in the case where a small-scale computer system, including a personal computer or the like, is used.